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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Aliasing01:18

Aliasing

717
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
630
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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Perception of Sound Waves01:01

Perception of Sound Waves

5.9K
The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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Real-Time Signal Processing for Distributed Acoustic Sensing and Acoustic Sensing Systems Under Non-Stationary Noise.

Samuel Yaw Mensah1, Tao Zhang2, Xin Zhao2

  • 1School of Information Engineering, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China.

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Summary

This study introduces a Unified Bayesian-Kalman Estimator (UBKE) for real-time acoustic enhancement in challenging non-stationary noise. The UBKE adaptively fuses spectral and temporal information, significantly improving signal quality with low latency.

Keywords:
Bayesian estimationKalman filteringacoustic sensingdistributed acoustic sensingnon-stationary noisereal-time signal processingspeech enhancement

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • Real-time acoustic enhancement in non-stationary noise is difficult for causal, low-latency systems.
  • Existing methods struggle to balance spectral and temporal information effectively.

Purpose of the Study:

  • To propose a Unified Bayesian-Kalman Estimator (UBKE) for causal, low-latency acoustic signal enhancement.
  • To analytically fuse spectral and temporal estimation techniques for adaptive noise reduction.

Main Methods:

  • Developed a closed-form UBKE by integrating a Bayesian Minimum Mean Square Error (MMSE) estimator with a Kalman state-space tracker.
  • Utilized a variance-optimal fusion weight (α(k)) to adaptively balance spectral and temporal information.
  • Analyzed theoretical properties including bias-variance, stability, and performance metrics (SNR, log-spectral distortion).

Main Results:

  • UBKE operates causally with a 16 ms delay and real-time factors below 0.5.
  • Achieved up to +9.8 dB SNR improvement and ~17% PESQ gain over baseline MMSE in non-stationary noise.
  • Demonstrated close agreement between analytical predictions and empirical results on speech corpora.

Conclusions:

  • UBKE provides an interpretable, low-latency framework for real-time acoustic sensing and speech enhancement.
  • The method adaptively balances spectral and temporal information, outperforming traditional MMSE estimators.
  • Serves as a foundation for future hybrid model-driven and learning-augmented acoustic systems.